AF 2018 Analysis
Ewan Davies
4 September 2018
- 0.1 Metadata
- 0.2 Likert data for 2018 HECC questions
- 0.3 A comparison of HECC questions 2016 vs. 2018
- 0.4 HECC Questions Faceted by Location
- 0.5 HECC Questions Faceted by Entity
- 0.6 Analysis of HECC questions by Entity, Governmental vs. Research Organizations
- 0.7 Analysis of HECC questions by Location, Africa vs. Other
- 0.8 Analysis of HECC questions by Location, Africa vs. Europe
- 0.9 Analysis of HECC questions by Location, North vs. South
- 0.10 HECC 2016 Questions Faceted by Location
- 0.11 Analysis of 2016 HECC questions by Location, Europe vs. Other
- 0.12 Analysis of 2016 HECC questions combined by Location, North vs. South
- 0.13 2016 and 2018 data combined, Africa vs. Europe
- 0.14 Cluster Analysis for HECC Questions
0.1 Metadata
There were 198 responses to the AF2018 Survey.
0.1.1 Box Plot of Response Times
Note that due to extreme outliers only response times of over 5 minutes and under 90 minutes are considered here. (37 responses omitted)
The mean response time of these respondents was 19 minutes 6 seconds The minimum response time was 5 minutes 8 seconds The maximum response time was 84 minutes 32 seconds
## NULL0.2 Likert data for 2018 HECC questions
0.3 A comparison of HECC questions 2016 vs. 2018
The chi-square test finds the probability (p) of the distribution of answers to a question remaining the same in 2018 as it was in 2016. Note: This assumes the data is categorical with no repeat answers, so we assume the people answering the survey are not the same in 2016 as in 2018. This is reasonable in a sample size as large as ours.
The Wilcoxon-Mann-Whitney test tests whether there has been a general shift in opinion rather than a change in the proportions of people falling into categories.
0.3.1 Q17.1: In my work I am considering climate changes of more than 2 degrees C
Neither test finds a significant difference in responses for this question.
##
## Pearson's Chi-squared test
##
## data: Q17.1_summarised[, -c(1)]
## X-squared = 6.7796, df = 3, p-value = 0.07926##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: value by variable (2016, 2018)
## Z = 1.4845, p-value = 0.1377
## alternative hypothesis: true mu is not equal to 00.3.2 Q17.2: I am using high end climate change scenarios for decision-making processes in the context of adaptation/mitigation
Neither test finds a significant difference in responses for this question.
##
## Pearson's Chi-squared test
##
## data: Q17.2_summarised[, -c(1)]
## X-squared = 2.9519, df = 3, p-value = 0.3991##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: value by variable (2016, 2018)
## Z = -0.69402, p-value = 0.4877
## alternative hypothesis: true mu is not equal to 00.3.3 Q18.1: The likelihood of high-end climate change now becoming reality has decreased after the Paris agreement
Both the chi-square and WMW tests show a significant shift to disagreement with this statement, with both strongly disagree and disagree increasing in proportion greatly, with these numbers cominng from the neutral category, as the the agree and strongly agree category are largely unchanged.
##
## Pearson's Chi-squared test
##
## data: Q18.1_summarised[, -c(1)]
## X-squared = 27.43, df = 4, p-value = 1.627e-05##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: value by variable (2016, 2018)
## Z = 3.7362, p-value = 0.0001868
## alternative hypothesis: true mu is not equal to 00.3.4 Q18.2: We should wait with large-scale adaptation activities until we have more certainty about the impacts of high-end climate change
This plot shows that while the proportions of neutral, agree and strongly agree have only decreased slightly, there has a been a significant increase in the strongly disagree proportion and a large reduction in the diagree proportion. It seems people disagree more strongly in 2018 than 2016 and the WMW test shows that this shift is statistically significant.
## Warning in chisq.test(Q18.2_summarised[, -c(1)]): Chi-squared approximation
## may be incorrect##
## Pearson's Chi-squared test
##
## data: Q18.2_summarised[, -c(1)]
## X-squared = 19.142, df = 4, p-value = 0.0007369##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: value by variable (2016, 2018)
## Z = 3.3274, p-value = 0.0008766
## alternative hypothesis: true mu is not equal to 00.3.5 Q18.3: We, as a society, have tools in place to deal with impacts from high-end climate change
Neither test finds a significant difference in responses for this question.
##
## Pearson's Chi-squared test
##
## data: Q18.3_summarised[, -c(1)]
## X-squared = 2.7112, df = 4, p-value = 0.6072##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: value by variable (2016, 2018)
## Z = 1.2131, p-value = 0.2251
## alternative hypothesis: true mu is not equal to 00.3.6 Q19.1: It is easy to find information about potential impacts of high-end climate change
We see a significant shift from the agree and neutral categories to the disagree category with the extremities changing very little giving a significant result from the chi-square test, however the WMW test finds no significant evidence of an overall shift in opinion.
##
## Pearson's Chi-squared test
##
## data: Q19.1_summarised[, -c(1)]
## X-squared = 12.602, df = 4, p-value = 0.0134##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: value by variable (2016, 2018)
## Z = 1.5782, p-value = 0.1145
## alternative hypothesis: true mu is not equal to 00.3.7 Q19.2: It is easy to find information about decision making with respect to impacts, adaptation and vulnerability in the context of high-end climate change
We see a significant polarisation between 2016 and 2018 with more extreme responses in 2018, giving a significant result in the chi-square test, however there is no evidence of an opinion shift in the WMW test.
## Warning in chisq.test(Q19.2_summarised[, -c(1)]): Chi-squared approximation
## may be incorrect##
## Pearson's Chi-squared test
##
## data: Q19.2_summarised[, -c(1)]
## X-squared = 9.8775, df = 4, p-value = 0.04254##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: value by variable (2016, 2018)
## Z = 0.20946, p-value = 0.8341
## alternative hypothesis: true mu is not equal to 00.3.8 An overview of Statistical Test results comparing 2016/18
The Chi-square test has found a significant difference in questions 18.1 (p = 1.812e-05), 18.2(p = 0.0009099), 19.1(p = 0.01172) and 19.2(p = 0.04834). There is a possible issue in 18.2 where too few (3) people selected “strongly agree” to be sure the approximations made in the test are accurate, though much literature says that as long as the number is greater than 1, the approximation is good enough in a large sample.
The Wilcoxon-Mann-Whitney test has found a significant difference in questions 18.1 (p-value = 0.0001868), 18.2 (p-value = 0.0008766) with responses to both of these questions becoming more negative in 2018 than they were in 2016.
0.4 HECC Questions Faceted by Location
0.5 HECC Questions Faceted by Entity
0.6 Analysis of HECC questions by Entity, Governmental vs. Research Organizations
Respondents belonging to entities “Government”, “Government agency” and “Public policy” are categorised as “Gov Related” and are compared to those belonging to the “Research organization” category. Note: Available responses were “Government agency”, “Research organization”, “consultancy”, “NGO”, “Government”, “Business” and “Public policy”.
Both the chi-square and Wilcoxon-Mann-Whitney tests show no evidence that respondents from Governmental and research organizations answered the HECC questions differently.
## Entity
## Q17.1 Gov Related Research
## All the time 9 25
## Not at all 1 11
## Occasionally 8 18
## We are starting to 5 19## Warning in chisq.test(table(HECC_Entity_grouped[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped[, c(x + 1, 1)])
## X-squared = 2.5166, df = 3, p-value = 0.4723
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.43656, p-value = 0.6624
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q17.2 Gov Related Research
## All the time 6 24
## Not at all 1 17
## Occasionally 9 17
## We are starting to 6 15## Warning in chisq.test(table(HECC_Entity_grouped[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped[, c(x + 1, 1)])
## X-squared = 5.5661, df = 3, p-value = 0.1347
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.31882, p-value = 0.7499
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q18.1 Gov Related Research
## Agree 5 13
## Disagree 7 26
## Neither agree nor disagree 6 15
## Strongly Agree 1 3
## Strongly disagree 4 17## Warning in chisq.test(table(HECC_Entity_grouped[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped[, c(x + 1, 1)])
## X-squared = 0.80888, df = 4, p-value = 0.9373
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.55095, p-value = 0.5817
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q18.2 Gov Related Research
## Agree 3 5
## Disagree 6 26
## Neither agree nor disagree 3 5
## Strongly Agree 2 3
## Strongly disagree 9 35## Warning in chisq.test(table(HECC_Entity_grouped[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped[, c(x + 1, 1)])
## X-squared = 3.1085, df = 4, p-value = 0.5398
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.84734, p-value = 0.3968
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q18.3 Gov Related Research
## Agree 6 20
## Disagree 8 19
## Neither agree nor disagree 4 11
## Strongly Agree 3 5
## Strongly disagree 2 18## Warning in chisq.test(table(HECC_Entity_grouped[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped[, c(x + 1, 1)])
## X-squared = 3.4923, df = 4, p-value = 0.4791
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.71361, p-value = 0.4755
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q19.1 Gov Related Research
## Difficult 9 21
## Easy 5 19
## Neither easy nor difficult 5 24
## Very difficult 2 2
## Very Easy 2 7## Warning in chisq.test(table(HECC_Entity_grouped[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped[, c(x + 1, 1)])
## X-squared = 2.9518, df = 4, p-value = 0.5659
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = -1.357, p-value = 0.1748
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q19.2 Gov Related Research
## Difficult 11 28
## Easy 3 15
## Neither easy nor difficult 7 15
## Very difficult 1 12
## Very Easy 1 3## Warning in chisq.test(table(HECC_Entity_grouped[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped[, c(x + 1, 1)])
## X-squared = 3.5478, df = 4, p-value = 0.4707
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.35625, p-value = 0.7217
## alternative hypothesis: true mu is not equal to 00.6.1 Now Combine categories to improve chi-square approximation:
We combine “We are starting to” and “Occasionally” into an “In between” category for Q17.1/2, and combine the 5-point likert scales into 3 in the obvious way. This also shows no statistically significant differences between Government and Research organisations in both the chi-square and Wilcoxon-Mann-Whitney tests.
## Entity
## Q17.1 Gov Related Research
## All the time 9 25
## In between 13 37
## Not at all 1 11## Warning in chisq.test(table(HECC_Entity_grouped_combined[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped_combined[, c(x + 1, 1)])
## X-squared = 1.8403, df = 2, p-value = 0.3985
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.6851, p-value = 0.4933
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q17.2 Gov Related Research
## All the time 6 24
## In between 15 32
## Not at all 1 17## Warning in chisq.test(table(HECC_Entity_grouped_combined[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped_combined[, c(x + 1, 1)])
## X-squared = 5.3276, df = 2, p-value = 0.06968
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.50233, p-value = 0.6154
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q18.1 Gov Related Research
## Agree 6 16
## Disagree 11 43
## Neither agree nor disagree 6 15## Warning in chisq.test(table(HECC_Entity_grouped_combined[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped_combined[, c(x + 1, 1)])
## X-squared = 0.76169, df = 2, p-value = 0.6833
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.48691, p-value = 0.6263
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q18.2 Gov Related Research
## Agree 5 8
## Disagree 15 61
## Neither agree nor disagree 3 5## Warning in chisq.test(table(HECC_Entity_grouped_combined[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped_combined[, c(x + 1, 1)])
## X-squared = 3.0681, df = 2, p-value = 0.2157
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 1.5391, p-value = 0.1238
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q18.3 Gov Related Research
## Agree 9 25
## Disagree 10 37
## Neither agree nor disagree 4 11## Warning in chisq.test(table(HECC_Entity_grouped_combined[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped_combined[, c(x + 1, 1)])
## X-squared = 0.36371, df = 2, p-value = 0.8337
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = 0.065395, p-value = 0.9479
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q19.1 Gov Related Research
## Difficult 11 23
## Easy 7 26
## Neither easy nor difficult 5 24
##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped_combined[, c(x + 1, 1)])
## X-squared = 2.1699, df = 2, p-value = 0.3379
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = -1.1894, p-value = 0.2343
## alternative hypothesis: true mu is not equal to 0
##
## Entity
## Q19.2 Gov Related Research
## Difficult 12 40
## Easy 4 18
## Neither easy nor difficult 7 15
##
## Pearson's Chi-squared test
##
## data: table(HECC_Entity_grouped_combined[, c(x + 1, 1)])
## X-squared = 1.1711, df = 2, p-value = 0.5568
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by Entity (Gov Related, Research)
## Z = -0.13256, p-value = 0.8945
## alternative hypothesis: true mu is not equal to 00.7 Analysis of HECC questions by Location, Africa vs. Other
The chi-square test shows a significant difference in answers to Q17.2(p-value = 0.04651) and Q19.1(p-value = 0.01926), however the Wilcoxon-Mann-Whitney test shows this difference does not constitute a significant change in opinion. We do, however, see a significant shift in opinion for Q17.1 (p-value = 0.0482) in the WMW test.
## Location
## Q17.1 Africa Other
## All the time 23 39
## Not at all 13 11
## Occasionally 22 16
## We are starting to 25 15
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1[, c(x + 1, 1)])
## X-squared = 7.7198, df = 3, p-value = 0.05217
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Other)
## Z = -1.9756, p-value = 0.0482
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 Africa Other
## All the time 21 31
## Not at all 13 19
## Occasionally 25 17
## We are starting to 23 12
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1[, c(x + 1, 1)])
## X-squared = 7.9759, df = 3, p-value = 0.04651
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Other)
## Z = -0.36009, p-value = 0.7188
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 Africa Other
## Agree 20 8
## Disagree 24 38
## Neither agree nor disagree 16 15
## Strongly Agree 3 4
## Strongly disagree 16 19## Warning in chisq.test(table(HECC_location_grouped1[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1[, c(x + 1, 1)])
## X-squared = 8.5911, df = 4, p-value = 0.07217
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Other)
## Z = 1.8072, p-value = 0.07074
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 Africa Other
## Agree 7 5
## Disagree 26 26
## Neither agree nor disagree 8 5
## Strongly Agree 5 1
## Strongly disagree 34 47## Warning in chisq.test(table(HECC_location_grouped1[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1[, c(x + 1, 1)])
## X-squared = 5.6845, df = 4, p-value = 0.224
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Other)
## Z = 1.9301, p-value = 0.0536
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 Africa Other
## Agree 20 23
## Disagree 22 22
## Neither agree nor disagree 21 16
## Strongly Agree 7 5
## Strongly disagree 10 17
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1[, c(x + 1, 1)])
## X-squared = 2.9789, df = 4, p-value = 0.5614
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Other)
## Z = 0.91711, p-value = 0.3591
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 Africa Other
## Difficult 16 31
## Easy 24 17
## Neither easy nor difficult 28 27
## Very difficult 8 1
## Very Easy 5 7## Warning in chisq.test(table(HECC_location_grouped1[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1[, c(x + 1, 1)])
## X-squared = 11.756, df = 4, p-value = 0.01926
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Other)
## Z = 0.83158, p-value = 0.4056
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 Africa Other
## Difficult 25 39
## Easy 13 13
## Neither easy nor difficult 26 20
## Very difficult 12 9
## Very Easy 5 2## Warning in chisq.test(table(HECC_location_grouped1[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1[, c(x + 1, 1)])
## X-squared = 5.5358, df = 4, p-value = 0.2366
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Other)
## Z = 1.3758, p-value = 0.1689
## alternative hypothesis: true mu is not equal to 00.7.1 Q17.1: In my work I am considering climate changes of more than 2 degrees C
We see that due to the much higher proportion of respondents from outside Africa selecting “All the time” we have a significantly higher positive response from outside Africa than from in.
0.7.2 Q17.2: I am using high end climate change scenarios for decision-making processes in the context of adaptation/mitigation
Here we see a significant difference due to respondents from Africa selecting less extreme values than respondents from elsewhere.
0.7.3 Q19.1: It is easy to find information about potential impacts of high-end climate change
Here respondents from Africa responded more positively on the whole, however they also had a higher proportion of “very difficult” responses.
0.8 Analysis of HECC questions by Location, Africa vs. Europe
We see no evidence of a statistically significant difference in responses between Africa and Europe.
## Location
## Q17.1 Africa Europe
## All the time 23 14
## Not at all 13 4
## Occasionally 22 7
## We are starting to 25 6## Warning in chisq.test(table(HECC_location_Africa_Europe[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_Africa_Europe[, c(x + 1, 1)])
## X-squared = 3.3316, df = 3, p-value = 0.3433
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -1.4501, p-value = 0.147
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 Africa Europe
## All the time 21 13
## Not at all 13 8
## Occasionally 25 4
## We are starting to 23 5
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_Africa_Europe[, c(x + 1, 1)])
## X-squared = 7.2769, df = 3, p-value = 0.06358
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -0.50219, p-value = 0.6155
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 Africa Europe
## Agree 20 4
## Disagree 24 15
## Neither agree nor disagree 16 4
## Strongly Agree 3 1
## Strongly disagree 16 9## Warning in chisq.test(table(HECC_location_Africa_Europe[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_Africa_Europe[, c(x + 1, 1)])
## X-squared = 4.8246, df = 4, p-value = 0.3058
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 1.5201, p-value = 0.1285
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 Africa Europe
## Agree 7 1
## Disagree 26 11
## Neither agree nor disagree 8 3
## Strongly Agree 5 0
## Strongly disagree 34 18## Warning in chisq.test(table(HECC_location_Africa_Europe[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_Africa_Europe[, c(x + 1, 1)])
## X-squared = 3.9035, df = 4, p-value = 0.4192
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 1.0123, p-value = 0.3114
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 Africa Europe
## Agree 20 7
## Disagree 22 11
## Neither agree nor disagree 21 7
## Strongly Agree 7 2
## Strongly disagree 10 5## Warning in chisq.test(table(HECC_location_Africa_Europe[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_Africa_Europe[, c(x + 1, 1)])
## X-squared = 0.9787, df = 4, p-value = 0.913
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.6682, p-value = 0.504
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 Africa Europe
## Difficult 16 13
## Easy 24 8
## Neither easy nor difficult 28 8
## Very difficult 8 0
## Very Easy 5 3## Warning in chisq.test(table(HECC_location_Africa_Europe[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_Africa_Europe[, c(x + 1, 1)])
## X-squared = 8.2191, df = 4, p-value = 0.08387
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.29131, p-value = 0.7708
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 Africa Europe
## Difficult 25 14
## Easy 13 6
## Neither easy nor difficult 26 10
## Very difficult 12 1
## Very Easy 5 1## Warning in chisq.test(table(HECC_location_Africa_Europe[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_Africa_Europe[, c(x + 1, 1)])
## X-squared = 4.3342, df = 4, p-value = 0.3627
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.090236, p-value = 0.9281
## alternative hypothesis: true mu is not equal to 00.9 Analysis of HECC questions by Location, North vs. South
Here respondents from Europe, North America and Asia are grouped into a “North” category, and the rest into the “South” category.
The chi square test shows no significant differences in answers to the HECC questions between Northern/Southern hemisphere regions, however there is a significant difference in question 17.1 given by the WMW test (p-value = 0.02344)
## Location
## Q17.1 North South
## All the time 34 29
## Not at all 8 16
## Occasionally 13 26
## We are starting to 14 28
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2[, c(x + 1, 1)])
## X-squared = 6.9272, df = 3, p-value = 0.07425
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = 2.2662, p-value = 0.02344
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 North South
## All the time 25 29
## Not at all 16 17
## Occasionally 15 27
## We are starting to 11 25
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2[, c(x + 1, 1)])
## X-squared = 3.4989, df = 3, p-value = 0.3209
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -0.046304, p-value = 0.9631
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 North South
## Agree 7 21
## Disagree 32 33
## Neither agree nor disagree 13 19
## Strongly Agree 4 3
## Strongly disagree 16 19## Warning in chisq.test(table(HECC_location_grouped2[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2[, c(x + 1, 1)])
## X-squared = 5.4766, df = 4, p-value = 0.2418
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -1.2244, p-value = 0.2208
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 North South
## Agree 5 7
## Disagree 22 32
## Neither agree nor disagree 5 8
## Strongly Agree 0 7
## Strongly disagree 40 42## Warning in chisq.test(table(HECC_location_grouped2[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2[, c(x + 1, 1)])
## X-squared = 6.6331, df = 4, p-value = 0.1566
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -1.6424, p-value = 0.1005
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 North South
## Agree 19 26
## Disagree 18 26
## Neither agree nor disagree 15 22
## Strongly Agree 5 7
## Strongly disagree 14 15
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2[, c(x + 1, 1)])
## X-squared = 0.50436, df = 4, p-value = 0.9731
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -0.41172, p-value = 0.6805
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 North South
## Difficult 26 24
## Easy 15 26
## Neither easy nor difficult 23 32
## Very difficult 1 8
## Very Easy 6 7## Warning in chisq.test(table(HECC_location_grouped2[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2[, c(x + 1, 1)])
## X-squared = 6.1488, df = 4, p-value = 0.1883
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -0.4739, p-value = 0.6356
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 North South
## Difficult 34 31
## Easy 10 16
## Neither easy nor difficult 18 29
## Very difficult 7 15
## Very Easy 2 6## Warning in chisq.test(table(HECC_location_grouped2[, c(x + 1, 1)])): Chi-
## squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2[, c(x + 1, 1)])
## X-squared = 5.1051, df = 4, p-value = 0.2767
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -1.2252, p-value = 0.2205
## alternative hypothesis: true mu is not equal to 00.9.1 Q17.1: In my work I am considering climate changes of more than 2 degrees C
We see a signicantly more positive response to Q17.1 from Northern responents.
0.9.2 Now Combine categories to improve chi-square approximation:
We now see a statistically significant (p-value = 0.03132 for chi square, p-value = 0.01587 for WMW) difference between North and South in Q17.1(In my work I am considering climate changes of more than 2 degrees C), showing a higher proportion of people in the Northern Hemisphere are considering HECC in their work “All the time” vs. “In between”.
## Location
## Q17.1 North South
## All the time 34 29
## In between 27 54
## Not at all 8 16
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_combined[, c(x + 1, 1)])
## X-squared = 6.9272, df = 2, p-value = 0.03132
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = 2.4119, p-value = 0.01587
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 North South
## All the time 25 29
## In between 26 52
## Not at all 16 17
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_combined[, c(x + 1, 1)])
## X-squared = 3.285, df = 2, p-value = 0.1935
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = 0.21037, p-value = 0.8334
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 North South
## Agree 11 24
## Disagree 48 52
## Neither agree nor disagree 13 19
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_combined[, c(x + 1, 1)])
## X-squared = 3.0029, df = 2, p-value = 0.2228
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -1.6932, p-value = 0.09042
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 North South
## Agree 5 14
## Disagree 62 74
## Neither agree nor disagree 5 8
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_combined[, c(x + 1, 1)])
## X-squared = 2.6396, df = 2, p-value = 0.2672
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -1.5327, p-value = 0.1254
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 North South
## Agree 24 33
## Disagree 32 41
## Neither agree nor disagree 15 22
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_combined[, c(x + 1, 1)])
## X-squared = 0.11503, df = 2, p-value = 0.9441
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -0.22265, p-value = 0.8238
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 North South
## Difficult 27 32
## Easy 21 33
## Neither easy nor difficult 23 32
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_combined[, c(x + 1, 1)])
## X-squared = 0.55255, df = 2, p-value = 0.7586
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -0.73924, p-value = 0.4598
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 North South
## Difficult 41 46
## Easy 12 22
## Neither easy nor difficult 18 29
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_combined[, c(x + 1, 1)])
## X-squared = 1.8229, df = 2, p-value = 0.402
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -1.3368, p-value = 0.1813
## alternative hypothesis: true mu is not equal to 00.9.3 Q17.1: In my work I am considering climate changes of more than 2 degrees C
0.10 HECC 2016 Questions Faceted by Location
0.11 Analysis of 2016 HECC questions by Location, Europe vs. Other
Note: in tables below, the scales are simply ranked from low (1) to high (4/5)
We find no significant evidence that questions were answered differently in Europe in 2016
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion## Location
## Q17.1 Europe Other
## 1 8 6
## 2 24 15
## 3 16 8
## 4 54 17## Warning in chisq.test(table(HECC_location_grouped1_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_num[, c(x + 1, 1)])
## X-squared = 3.6434, df = 3, p-value = 0.3026
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 1.9008, p-value = 0.05733
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 Europe Other
## 1 18 14
## 2 23 11
## 3 32 10
## 4 26 12
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_num[, c(x + 1, 1)])
## X-squared = 3.3173, df = 3, p-value = 0.3452
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 1.1866, p-value = 0.2354
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 Europe Other
## 1 10 0
## 2 33 14
## 3 43 21
## 4 17 9
## 5 4 5## Warning in chisq.test(table(HECC_location_grouped1_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_num[, c(x + 1, 1)])
## X-squared = 7.2547, df = 4, p-value = 0.123
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -1.9314, p-value = 0.05344
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 Europe Other
## 1 33 9
## 2 49 25
## 3 13 5
## 4 10 8
## 5 2 1## Warning in chisq.test(table(HECC_location_grouped1_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_num[, c(x + 1, 1)])
## X-squared = 3.685, df = 4, p-value = 0.4503
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -1.5222, p-value = 0.1279
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 Europe Other
## 1 17 3
## 2 25 14
## 3 22 9
## 4 35 18
## 5 7 3## Warning in chisq.test(table(HECC_location_grouped1_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_num[, c(x + 1, 1)])
## X-squared = 3.1192, df = 4, p-value = 0.5381
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -0.81562, p-value = 0.4147
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 Europe Other
## 1 6 3
## 2 17 6
## 3 38 24
## 4 35 14
## 5 6 0## Warning in chisq.test(table(HECC_location_grouped1_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_num[, c(x + 1, 1)])
## X-squared = 4.7701, df = 4, p-value = 0.3117
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 0.84077, p-value = 0.4005
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 Europe Other
## 1 9 5
## 2 38 18
## 3 43 20
## 4 13 3
## 5 2 0## Warning in chisq.test(table(HECC_location_grouped1_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_num[, c(x + 1, 1)])
## X-squared = 2.2182, df = 4, p-value = 0.6957
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 0.99008, p-value = 0.3221
## alternative hypothesis: true mu is not equal to 00.11.1 Now Combine categories to improve chi-square approximation:
This still shows no significant difference between Europe and elsewhere in 2016
## Location
## Q17.1 Europe Other
## All the time 54 17
## In between 40 23
## Not at all 8 6## Warning in chisq.test(table(HECC_location_grouped1_16_combined[, c(x + 1, :
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 3.461, df = 2, p-value = 0.1772
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 1.8533, p-value = 0.06384
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 Europe Other
## All the time 26 12
## In between 55 21
## Not at all 18 14
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 2.689, df = 2, p-value = 0.2607
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 0.97879, p-value = 0.3277
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 Europe Other
## Agree 21 14
## Disagree 43 14
## Neither agree nor disagree 43 21
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 2.4981, df = 2, p-value = 0.2868
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -1.5752, p-value = 0.1152
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 Europe Other
## Agree 12 9
## Disagree 82 34
## Neither agree nor disagree 13 5
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 1.6233, df = 2, p-value = 0.4441
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -0.90448, p-value = 0.3657
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 Europe Other
## Agree 42 21
## Disagree 42 17
## Neither agree nor disagree 22 9
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 0.34441, df = 2, p-value = 0.8418
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -0.54443, p-value = 0.5861
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 Europe Other
## Difficult 23 9
## Easy 41 14
## Neither easy nor difficult 38 24
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 2.592, df = 2, p-value = 0.2736
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 0.63366, p-value = 0.5263
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 Europe Other
## Difficult 47 23
## Easy 15 3
## Neither easy nor difficult 43 20
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 1.8557, df = 2, p-value = 0.3954
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 0.94301, p-value = 0.3457
## alternative hypothesis: true mu is not equal to 00.12 Analysis of 2016 HECC questions combined by Location, North vs. South
The WMW test shows that respondents from the north more strongly disagree in 18.2 (We should wait with large-scale adaptation activities until we have more certainty about the impacts of high-end climate change) but no other questions yield statistically significant differences.
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion## Location
## Q17.1 North South
## 1 12 2
## 2 33 6
## 3 19 5
## 4 63 8## Warning in chisq.test(table(HECC_location_grouped2_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_16_num[, c(x + 1, 1)])
## X-squared = 1.4138, df = 3, p-value = 0.7023
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = 0.67309, p-value = 0.5009
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 North South
## 1 26 6
## 2 29 5
## 3 39 3
## 4 30 8## Warning in chisq.test(table(HECC_location_grouped2_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_16_num[, c(x + 1, 1)])
## X-squared = 3.4671, df = 3, p-value = 0.325
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -0.067871, p-value = 0.9459
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 North South
## 1 10 0
## 2 40 7
## 3 56 8
## 4 23 3
## 5 6 3## Warning in chisq.test(table(HECC_location_grouped2_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_16_num[, c(x + 1, 1)])
## X-squared = 4.8224, df = 4, p-value = 0.306
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -0.90381, p-value = 0.3661
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 North South
## 1 40 2
## 2 62 12
## 3 15 3
## 4 13 5
## 5 3 0## Warning in chisq.test(table(HECC_location_grouped2_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_16_num[, c(x + 1, 1)])
## X-squared = 6.6302, df = 4, p-value = 0.1568
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -2.0636, p-value = 0.03906
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 North South
## 1 19 1
## 2 30 9
## 3 29 2
## 4 43 10
## 5 10 0## Warning in chisq.test(table(HECC_location_grouped2_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_16_num[, c(x + 1, 1)])
## X-squared = 7.9547, df = 4, p-value = 0.09325
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = 0.018861, p-value = 0.985
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 North South
## 1 7 2
## 2 21 2
## 3 53 9
## 4 40 9
## 5 6 0## Warning in chisq.test(table(HECC_location_grouped2_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_16_num[, c(x + 1, 1)])
## X-squared = 2.6186, df = 4, p-value = 0.6235
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = -0.25262, p-value = 0.8006
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 North South
## 1 11 3
## 2 48 8
## 3 52 11
## 4 16 0
## 5 2 0## Warning in chisq.test(table(HECC_location_grouped2_16_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped2_16_num[, c(x + 1, 1)])
## X-squared = 4.0255, df = 4, p-value = 0.4026
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (North, South)
## Z = 1.0576, p-value = 0.2903
## alternative hypothesis: true mu is not equal to 00.12.1 Q18.2: We should wait with large-scale adaptation activities until we have more certainty about the impacts of high-end climate change
0.12.2 Now Combine categories to improve chi-square approximation:
This finds no significant differences between North/South responses in 2016
## Location
## Q17.1 Europe Other
## All the time 54 17
## In between 40 23
## Not at all 8 6## Warning in chisq.test(table(HECC_location_grouped1_16_combined[, c(x + 1, :
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 3.461, df = 2, p-value = 0.1772
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 1.8533, p-value = 0.06384
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 Europe Other
## All the time 26 12
## In between 55 21
## Not at all 18 14
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 2.689, df = 2, p-value = 0.2607
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 0.97879, p-value = 0.3277
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 Europe Other
## Agree 21 14
## Disagree 43 14
## Neither agree nor disagree 43 21
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 2.4981, df = 2, p-value = 0.2868
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -1.5752, p-value = 0.1152
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 Europe Other
## Agree 12 9
## Disagree 82 34
## Neither agree nor disagree 13 5
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 1.6233, df = 2, p-value = 0.4441
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -0.90448, p-value = 0.3657
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 Europe Other
## Agree 42 21
## Disagree 42 17
## Neither agree nor disagree 22 9
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 0.34441, df = 2, p-value = 0.8418
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = -0.54443, p-value = 0.5861
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 Europe Other
## Difficult 23 9
## Easy 41 14
## Neither easy nor difficult 38 24
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 2.592, df = 2, p-value = 0.2736
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 0.63366, p-value = 0.5263
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 Europe Other
## Difficult 47 23
## Easy 15 3
## Neither easy nor difficult 43 20
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_grouped1_16_combined[, c(x + 1, 1)])
## X-squared = 1.8557, df = 2, p-value = 0.3954
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Europe, Other)
## Z = 0.94301, p-value = 0.3457
## alternative hypothesis: true mu is not equal to 00.13 2016 and 2018 data combined, Africa vs. Europe
Here we have combined the 2016/18 data and we compare the differences between Europe and Africa. Due to the similarity of the responses to questions 17.1/2 in 2016 and 2018, this is a valid way to obtain a population with a large sample size comparing Africa and Europe for these questions. It could be argued that this is not valid for questions with significant differences in responses, but we include analysis of all questions here.
We see a statistically significant difference in Q17.1 (In my work I am considering climate changes of more than 2 degrees C) in both tests, with Europe considering HECC significantly more frequently than Africa
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion
## Warning in function_list[[k]](value): NAs introduced by coercion## Location
## Q17.1 Africa Europe
## 1 14 12
## 2 25 31
## 3 27 22
## 4 25 68
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_num[, c(x + 1, 1)])
## X-squared = 13.799, df = 3, p-value = 0.003192
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -2.9793, p-value = 0.00289
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 Africa Europe
## 1 16 26
## 2 27 27
## 3 25 37
## 4 22 39
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_num[, c(x + 1, 1)])
## X-squared = 2.5778, df = 3, p-value = 0.4614
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -0.74922, p-value = 0.4537
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 Africa Europe
## 1 16 19
## 2 26 48
## 3 19 47
## 4 22 21
## 5 4 5## Warning in chisq.test(table(HECC_location_combined_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_num[, c(x + 1, 1)])
## X-squared = 6.8075, df = 4, p-value = 0.1464
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.40626, p-value = 0.6846
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 Africa Europe
## 1 35 51
## 2 28 60
## 3 9 16
## 4 11 11
## 5 5 2## Warning in chisq.test(table(HECC_location_combined_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_num[, c(x + 1, 1)])
## X-squared = 6.3283, df = 4, p-value = 0.1759
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.49062, p-value = 0.6237
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 Africa Europe
## 1 10 22
## 2 26 36
## 3 22 29
## 4 23 42
## 5 7 9
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_num[, c(x + 1, 1)])
## X-squared = 1.909, df = 4, p-value = 0.7525
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.19681, p-value = 0.844
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 Africa Europe
## 1 9 6
## 2 17 30
## 3 31 46
## 4 27 43
## 5 5 9
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_num[, c(x + 1, 1)])
## X-squared = 2.9575, df = 4, p-value = 0.565
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -0.68131, p-value = 0.4957
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 Africa Europe
## 1 12 10
## 2 28 52
## 3 31 53
## 4 13 19
## 5 5 3## Warning in chisq.test(table(HECC_location_combined_num[, c(x + 1, 1)])):
## Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_num[, c(x + 1, 1)])
## X-squared = 4.7901, df = 4, p-value = 0.3095
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.024136, p-value = 0.9807
## alternative hypothesis: true mu is not equal to 00.13.1 17.1: In my work I am considering climate changes of more than 2 degrees C
0.13.2 Now Combine categories to improve chi-square approximation:
We again see that only Q17.1 gives a significant difference
## Location
## Q17.1 Africa Europe
## All the time 25 68
## In between 52 53
## Not at all 14 12
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_combined[, c(x + 1, 1)])
## X-squared = 12.614, df = 2, p-value = 0.001824
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -3.4471, p-value = 0.0005667
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q17.2 Africa Europe
## All the time 22 39
## In between 52 64
## Not at all 16 26
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_combined[, c(x + 1, 1)])
## X-squared = 1.4612, df = 2, p-value = 0.4816
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -0.41102, p-value = 0.6811
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.1 Africa Europe
## Agree 26 26
## Disagree 42 67
## Neither agree nor disagree 19 47
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_combined[, c(x + 1, 1)])
## X-squared = 5.5403, df = 2, p-value = 0.06265
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.76201, p-value = 0.4461
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.2 Africa Europe
## Agree 16 13
## Disagree 63 111
## Neither agree nor disagree 9 16
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_combined[, c(x + 1, 1)])
## X-squared = 3.8525, df = 2, p-value = 0.1457
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 1.5085, p-value = 0.1314
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q18.3 Africa Europe
## Agree 30 51
## Disagree 36 58
## Neither agree nor disagree 22 29
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_combined[, c(x + 1, 1)])
## X-squared = 0.51755, df = 2, p-value = 0.772
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -0.1297, p-value = 0.8968
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.1 Africa Europe
## Difficult 26 36
## Easy 32 52
## Neither easy nor difficult 31 46
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_combined[, c(x + 1, 1)])
## X-squared = 0.22534, df = 2, p-value = 0.8934
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = -0.47341, p-value = 0.6359
## alternative hypothesis: true mu is not equal to 0
##
## Location
## Q19.2 Africa Europe
## Difficult 40 62
## Easy 18 22
## Neither easy nor difficult 31 53
##
## Pearson's Chi-squared test
##
## data: table(HECC_location_combined_combined[, c(x + 1, 1)])
## X-squared = 0.74596, df = 2, p-value = 0.6887
##
##
## Asymptotic Wilcoxon-Mann-Whitney Test
##
## data: Value by location (Africa, Europe)
## Z = 0.34982, p-value = 0.7265
## alternative hypothesis: true mu is not equal to 00.14 Cluster Analysis for HECC Questions
Cluster analysis aims to group data so that data points in each cluster are more similar to points in their cluster than they are to those in other clusters. The algorithm used here uses a Jaccard similarity measure to find the best clustering in a data set.
0.14.1 Cluster Analysis for Question 18
The cluster analysis finds 4 clusters, 3 (clusters 1,2,3) of which with high (>0.75) Clusterwise Jaccard bootstrap, indicating stable clusters. Clusters 2 and 3 are in strong agreement for Q18.1 and Q18.2, showing similarly negative responses, but diverging on Q18.3 with cluster 2 strongly agreeing and cluster 3 strongly disagreeing.
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## boot 100## * Cluster stability assessment *
## Cluster method: kmeans
## Full clustering results are given as parameter result
## of the clusterboot object, which also provides further statistics
## of the resampling results.
## Number of resampling runs: 100
##
## Number of clusters found in data: 4
##
## Clusterwise Jaccard bootstrap (omitting multiple points) mean:
## [1] 0.8078009 0.8698806 0.8694783 0.6874373
## dissolved:
## [1] 5 4 2 25
## recovered:
## [1] 72 87 86 470.14.2 Question 18 Responses by cluster
Note: the two plots are the same data regrouped differently, one by question, one by cluster.
0.14.3 Analysis of Q18 Clusters
Cluster 1 lean towards agreement with every statement, with few exceptions and contains 25 responses.
Cluster 2 contains 52 responses, disagrees strongly with Q18.1/2 but agrees with Q18.3
Cluster 3, containing 48 responses, almost completely disagrees with all Questions.
Cluster 4 was not a stable cluster (clusterwise Jaccard ~ 0.69) so is not particularly meaningful
0.14.4 Repeat 2016 Analysis (Q17.1, Q18.1/3, Q19.1)
In 2016 we saw two clusters, which diverged on H7 (Q18.3). Again we see that Q18.3 is the most divisive question, with clusters agreeing quite well for Q18.1, though we also see some divergence for Q17.1 and Q19.1 also.
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## boot 100## * Cluster stability assessment *
## Cluster method: kmeans
## Full clustering results are given as parameter result
## of the clusterboot object, which also provides further statistics
## of the resampling results.
## Number of resampling runs: 100
##
## Number of clusters found in data: 2
##
## Clusterwise Jaccard bootstrap (omitting multiple points) mean:
## [1] 0.8103102 0.7838500
## dissolved:
## [1] 15 18
## recovered:
## [1] 73 64
